Applying Deep Graph Representation Learning to the Malware Graph
CAMLIS 2019, Bayan Bruss
Applying Deep Graph Representation Learning to the Malware Graph (abstract: https://www.camlis.org/2019/talks/bruss)
Видео Applying Deep Graph Representation Learning to the Malware Graph канала CAMLIS
Applying Deep Graph Representation Learning to the Malware Graph (abstract: https://www.camlis.org/2019/talks/bruss)
Видео Applying Deep Graph Representation Learning to the Malware Graph канала CAMLIS
Показать
Комментарии отсутствуют
Информация о видео
Другие видео канала
SQL Driven Infrastructure for Cybersecurity ML Operations
Using Anomaly Detection on User Demographic Distributions to Identify Fake Account Bursts
Threat Detection on Kubernetes Logs Using GNN Embeddings
MalDICT: Benchmark Datasets on Malware Behaviors, Platforms, Exploitation, and Packers
Minimizing Compute Costs: When When Should We Run More Expensive Malware Analysis? (CAMLIS 2022)
An Effective Framework for Malware Detection and Classification using Feature Prioritization
FASER: Binary Code Similarity Search through the use of Intermediate Representations
Activation Analysis of a Byte-based Deep Neural Network for Malware Classification
LLM Prompt Injection: Attacks and Defenses
Exploring Backdoor Poisoning Attacks Against Malware Classifiers
Compilation as a Defense: Enhancing DL Model Attack Robustness via Tensor Optimization
A feature presentation: semi-supervised learning of file representations
Playing Cat and Mouse with the Attacker: Frequent Item Set Mining in the Registry (CAMLIS 2022)
Keynote: Lessons Learned in Red Teaming AI Systems in High-Stakes Environments (CAMLIS 2022)
Next Generation Process Emulation with Binee
Describing Malware via Tagging
EMBER Improvements
ProblemChild: Discovering Anomalous Patterns based on Parent-Child Process Relationships
Playing Defense: Benchmarking Cybersecurity Capabilities of Large Language Models
Inroads in Autonomous Network Defence using Explained Reinforcement Learning (CAMLIS 2022)
Keynote - Lessons for AI Security Preparedness